The Internet of Things (IoT) extends the Internet to include also wireless embedded computers that are often equipped with sensors and actuators to monitor and control their physical environment. The IoT is increasingly used for safety-critical applications such as smart factories or networked cars, where a failure of the IoT may lead to catastrophic consequences. The IoT is therefore in urgent need of dependability, where reliability, availability, and security properties can be guaranteed even in harsh environments (e.g., radio interference) and under deliberate attacks (e.g., exploiting side channels). In this paper we give an overview of recent research activities in the LEAD project “Dependable Internet of Things in Adverse Environments” towards a dependable IoT, specifically dependable wireless communication and localization using Ultra-Wide-Band technology, secure execution of real-time software, protocol testing and verification, and dependable networked control. We also present the TruckLab testbed, where our research results can be integrated and validated in a platooning use case. In this testbed, model trucks are automatically controlled to follow a lead truck.

Porous acoustic absorbers have excellent properties in the low-frequency range when positioned in room edges, therefore they are a common method for reducing low-frequency reverberation. However, standard room acoustic simulation methods such as ray tracing and mirror sources are invalid for low frequencies in general which is a consequence of using geometrical methods, yielding a lack of simulation tools for these so-called edge absorbers. In this article, a validated finite element simulation model is presented, which is able to predict the effect of an edge absorber on the acoustic field. With this model, the interaction mechanisms between room and absorber can be studied by high-resolved acoustic field visualizations in both room and absorber. The finite element model is validated against transfer function data computed from impulse response measurements in a reverberation chamber in style of ISO 354. The absorber made of Basotect^® is modeled using the Johnson-Champoux-Allard-Lafarge model, which is fitted to impedance tube measurements using the four-microphone transfer matrix method. It is shown that the finite element simulation model is able to predict the influence of different edge absorber configurations on the measured transfer functions to a high degree of accuracy. The evaluated third-octave band error exhibits deviations of 3.3–4.1 dB computed from third-octave band averaged spectra.

The information bottleneck (IB) framework, proposed in

We introduce a new and intuitive algorithm to characterize and localize multiple harmonic sources intersecting in the spatial and frequency domains. It jointly estimates their fundamental frequencies, their respective amplitudes, and their directions of arrival based on an intelligent non-parametric signal representation. To obtain these parameters, we first apply variable-scale sampling on unbiased cross-correlation functions between pairs of microphone signals to generate a joint parameter space. Then, we employ a multidimensional maxima detector to represent the parameters in a sparse joint parameter space. In comparison to others, our algorithm solves the issue of pitch-period doubling when using cross-correlation functions, it estimates multiple harmonic sources with a signal power smaller than the signal power of the dominant harmonic source, and it associates the estimated parameters to their corresponding sources in a multidimensional sparse joint parameter space, which can be directly fed into a tracker. We tested our algorithm and three others on synthetic data and speech data recorded in a real reverberant environment and evaluated their performance by employing the joint recall measure, the root-mean-square error, and the cumulative distribution function of fundamental frequencies and directions of arrival. The evaluations show promising results: Our algorithm outperforms the others in terms of the joint recall measure, and it can achieve root-mean-square errors of 1 Hz or 1$^\circ$ and smaller, which facilitates, e.g., distant-speech enhancement or source separation.